Production and perception of stop consonants in Spanish, Quichua, and Media Lengua
Bibliographic record
Abstract
This dissertation explores the phonetics and phonology of language contact, specifically pertaining to the integration of Spanish voiced stops /b/, /d/, and /g/ into Quichua, a language with non-contrastive stop voicing. Conflicting areas of convergence of this type appear when two or more phonological systems interact and phonemes from the target language are unknown natively to speakers of the source language. Media Lengua is a mixed language with an agglutinating Quichua morphology, and Quichua syntactic and phonological systems where nearly all the native Quichua vocabulary has been replaced by Spanish. This extreme contact scenario has integrated the voiced stop series into Media Lengua and abundant minimal pairs are present. If the phonological system of Media Lengua is indeed of Quichua origin however, how have speakers integrated the voiced stop series productively and perceptually? Have they adopted different strategies from Quichua speakers? If so, how do they differ? Chapter 1 sets the scene with an in-depth description of how contact between Spanish and Quichua has mutually influenced each language at the morphosyntactic level. Chapter 2 explores voice onset time (VOT) production in all five language varieties. Statistical modeling is used to search for differences in duration while taking into account a number of linguistic and demographic factors. Chapter 3 investigates stop perception in Media Lengua and Quichua, and uses Urban Spanish as a point of comparison. Chapter 4 looks at phonetic pre-nasalization in voiced stops across Media Lengua, Quichua, and Urban Spanish. Chapter 5 describes allophonic variations in stop production. The final chapter speculates on the nature of sound change at the phonetic level and explores possible origins of Media Lengua. Production results show that Media Lengua VOT duration values have shifted away from Quichua towards Rural Spanish. The perceptual results show an age-based effect with older Quichua speakers, which shows more random responses to the stimuli than younger speakers. This effect was not found in Media Lengua or Urban Spanish speakers. Similar age-based results were also found for stop weakening tendencies in Quichua and L2 Spanish speakers, while Media Lengua, Rural, and Urban Spanish speakers were not significantly affected by age.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".